The hfann package

[ Tags: ai, bsd3, deprecated, library ] [ Propose Tags ]

hfann is a Haskell binding to the Fast Artificial Neural Network (FANN) library It provides functions to easily create, train, test and use Artificial Neural Networks.

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Versions 0.1, 0.2, 0.3, 0.4, 0.4.1, 0.4.2
Change log CHANGELOG
Dependencies base (>=2 && <4) [details]
License BSD3
Copyright (c) Olivier Boudry 2008
Author Olivier Boudry
Maintainer Olivier Boudry <>
Category AI
Home page
Uploaded Thu Jun 25 08:47:41 UTC 2009 by OlivierBoudry
Distributions NixOS:0.4.2
Executables HFANNTest
Downloads 1737 total (116 in the last 30 days)
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Maintainer's Corner

For package maintainers and hackage trustees

Readme for hfann-0.2

[back to package description]
HFANN: A Haskell interface to the FANN library (
Copyright: 2008, Olivier Boudry
Licence: BSD3

This package was developped and tested with GHC-6.8.2 on the Win32 platform
and the FANN library version 2.0.

FANN (C-library) installation:
First of all you need to install the FANN library.

Download version 2.0 of the library from:
and build it according to the instruction found on:

On windows download the source version and build it using MinGW instead of
using the provided Visual C++ 6.0 Project File. Building with Visual C++ would
create DLLs and require the 'stdcall' calling convention which would not work.

Build and installation should be pretty straightforward:

    make install

HFANN (Haskell library) installation:
Edit the hfann.cabal file and adapt the 'include-dirs' and 'extra-lib-dirs'
parameters to your installation. On non windows platform just blanking those
two params should work as long as the library and includes are installed in
the lib and include places.

Build and install using Cabal:

    runghc Setup.lhs configure
    runghc Setup.lhs build
    runghc Setup.lhs haddock
    runghc Setup.lhs install

Note: if you have haddock < 2.0 skip the haddock step. src/HFANN/Data.hsc is
not parsed properly using earlier versions of haddock.

Using the library
You will find an example for training and using and artificial neural network
for the 'xor' function in the 'examples/xor' directory.

Complaints, feature requests and bug reports to: